EAGER: Leveling the Digital Playing Field for the Job Seeker

EAGER：为求职者打造公平的数字竞争环境

• 批准号: 1537768
• 负责人: Raquel Hill
• 金额: $ 28.98万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537768&HistoricalAwards=false
• 关键词: EAGER; Leveling; Digital; Playing; Field

This project aims to assess how online data impacts the hiring process. In an ideal situation, one might imagine that employers hire the most skilled applicant, but sociological research indicates that this may not be the case. A job applicant's similarity to the interviewer in class background and class-based leisure activities often matters as much or more to employers than a job applicant's skills or work experience. The ability of a recruiter or employer to learn such information from seemingly unrelated data has led researchers to express concerns about privacy, job relevance, and the potential for discrimination. This is the first stage in a larger project that aims to illuminate how online information impacts the ability of job seekers to find employment in post-recession United States. The project creates a framework for identifying systematic patterns of discrimination in regionally specific job markets, and also provide a fuller picture of precisely when in the hiring process are certain forms of discrimination likely to take place (upon submission of resume, at interview stage, and so on). In addition, the collected data will enable job seekers to discover how online information affects their employability, and aid the development of strategies to align online data and professional profiles. The researchers will develop a novel mixed-methods framework to better understand the hiring process and study hiring discrimination by combining ethnographic studies of employers and companies that aggregate applicant profiles; surveys of applicants' background, skillset, and job-seeking history; online profile aggregation; and traditional data mining techniques. This project will contribute to the understanding of how employment works in the United States, and the types of online information that may limit employability. The problem will be addressed across populations that have varying demographic profiles and skill sets, and whose primary industries vary greatly. The proposed analysis will capture regionally specific hiring practices and reveal insights into the kinds of demographic indicators that work for or against job seekers in different regions.

该项目旨在评估在线数据如何影响招聘过程。在理想情况下，人们可能会想象雇主雇用了最熟练的申请人，但是社会学研究表明，情况并非如此。求职者与班级背景和基于班级的休闲活动中与访调员的相似性通常对雇主重要或更重要，而不是求职者的技能或工作经验。招聘人员或雇主从看似无关的数据中学习此类信息的能力使研究人员表达了对隐私，工作相关性和歧视潜力的担忧。这是一个较大项目的第一阶段，旨在阐明在线信息如何影响求职者在美国衰退后美国找到就业的能力。该项目创建了一个框架，用于识别特定于区域特定的就业市场中的系统歧视模式，并且还提供了更全面的图景，即何时在招聘过程中可能发生某些形式的歧视形式（在简历，面试阶段提交后，等等）。 此外，收集到的数据将使求职者能够发现在线信息如何影响其就业能力，并帮助制定与在线数据和专业资料保持一致的策略。研究人员将开发一个新型的混合方法框架，以更好地了解招聘过程，并通过结合汇总申请人概况的雇主和公司的民族志研究来研究招聘歧视；调查申请人的背景，技能和寻求工作的历史；在线个人资料聚合；和传统的数据挖掘技术。该项目将有助于了解美国就业方式的工作方式，以及可能限制就业能力的在线信息的类型。这个问题将在具有不同人口概况和技能的人群之间解决，其主要产业差异很大。拟议的分析将捕获特定于区域的招聘实践，并揭示有关在不同地区为求职者服务或反对求职者的人口统计指标的见解。